Enhanced Model Training and API Integration

📅 2024-04-13 — Session: Enhanced Model Training and API Integration

🕒 00:05–23:50
🏷️ Labels: Machine Learning, Api Integration, Preprocessing, Model Retraining, Data Handling
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal

The session aimed to enhance the model training pipeline, improve API integration, and address preprocessing challenges in machine learning workflows.

Key Activities

• Model Saving and Naming Conventions: Discussed best practices for naming conventions to ensure clarity and organization when saving models.
• Model Training and Evaluation: Developed a pipeline for model training using SGDRegressor, including steps for evaluation and model saving.
• Data Handling Enhancements: Implemented methods for saving predictions to CSV for better data accessibility.
• API Integration: Enhanced the retrainModel and updatePlots functions in a web application, focusing on backend communication and frontend updates.
• Troubleshooting: Resolved issues related to undefined model names in API calls, float conversion errors, and handling unknown categories in data transformation.
• Preprocessing Pipeline: Integrated preprocessing steps in model predictions and addressed challenges with OneHotEncoder and ColumnTransformer pipelines.

Achievements

• Successfully implemented a comprehensive model training and evaluation pipeline.
• Improved API integration with enhanced backend and frontend communication.
• Resolved multiple data preprocessing and API-related issues, ensuring smoother operation of the machine learning workflows.

Pending Tasks

• Further optimization of the preprocessing pipeline to handle more complex data scenarios.
• Continuous monitoring and updating of the model retraining endpoint to incorporate new data and preprocessing steps.